Nomic Embed Code
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Code Embedding Model |
| Supported Languages | Python, Java, Ruby, PHP, JavaScript, Go |
| Paper | arXiv:2412.01007 |
| Model Access | Hugging Face |
What is nomic-embed-code?
Nomic Embed Code is a cutting-edge code embedding model designed for superior code retrieval performance. Built with 7 billion parameters, it represents a significant advancement in code understanding and retrieval capabilities, consistently outperforming other leading models like Voyage Code 3 and OpenAI Embed 3 Large across multiple programming languages.
Implementation Details
The model is trained on the carefully curated CoRNStack dataset, utilizing advanced techniques such as dual-consistency filtering and progressive hard negative mining. The architecture employs sophisticated approaches to ensure high-quality code representation and retrieval capabilities.
- Trained on filtered Stackv2 data with high-quality text-code pairs
- Implements dual-consistency filtering for noise reduction
- Uses curriculum-based hard negative mining
- Supports both transformers and sentence-transformers implementations
Core Capabilities
- Achieves state-of-the-art performance across 6 programming languages
- Excels particularly in Go (93.8%) and Ruby (81.8%) code retrieval
- Supports long-range dependencies with 256+ token docstrings
- Provides easy integration through popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive features include its large parameter count (7B), superior performance across multiple programming languages, and innovative training approach using dual-consistency filtering and progressive hard negative mining. It consistently outperforms other leading models in code retrieval tasks.
Q: What are the recommended use cases?
The model is ideal for code search and retrieval applications, semantic code understanding, and code-documentation matching. It's particularly effective for multilingual codebases and can be integrated into development tools for improved code search functionality.
